Embodiments disclosed herein relate to user-centric security classifications of cryptographic token sets that represent a prediction of whether a token set is malicious. A user-centric security classification of a token set is generated according to user behaviors that characterize users involved with the token set, including users holding tokens of the token set and users creating tokens of the token set. A model is trained to recognize user behaviors of certain users whose involvement with the token set increase the likelihood that the token set is non-malicious and secure. Such user behaviors that the model is trained to recognize include high time-rank behaviors, or holding onto cryptographic tokens for extended lengths of time. Upon generation of a user-centric security classification for a token set, graphical displays of tokens or of the token set are updated to reflect the user-centric security classification.
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4. The method of claim 3, wherein the predictive model is trained for generating the security classification in accordance with historical ground-truth training data that indicates whether or not each of a plurality of historical token sets were determined to perform malicious operations.
5. The method of claim 4, wherein the predictive model includes a machine learning model.
6. The method of claim 3, wherein the plurality of users includes one or more users associated with creation of cryptographic tokens of the particular token set, and wherein the security classification for the particular token set is generated based on the user-specific data for the one or more users associated with the creation of the cryptographic tokens of the particular token set.
7. The method of claim 3, wherein the security classification is generated to be a positive security classification that represents a predicted likelihood that the particular token set is not configured for performing malicious operations based at least on the user-specific data for each user indicating that each user is linked to at least a threshold number of different token sets.
8. The method of claim 3, wherein the security classification is generated to be a positive security classification that represents a predicted likelihood that the particular token set is not configured for performing malicious operations based at least on the user-specific data for each user indicating that each user is linked to at least a threshold number of different cryptographic tokens across the plurality of token sets that includes the particular token set.
9. The method of claim 3, wherein the security classification is generated to be a negative security classification that represents a predicted likelihood that the particular token set is configured for performing malicious operations based at least on the user-specific data for one or more of the plurality of particular users indicating an association between the one or more of the plurality of particular users and a token set having a respective negative security classification.
10. The method of claim 3, wherein the security classification is generated based on set-specific data including a number of different users and a length of time since a creation of the particular token set.
11. The method of claim 3, further comprising automatically updating a second graphical display of cryptographic tokens of the particular token set to indicate the security classification for the particular token set.
12. The method of claim 3, wherein the user-specific data for each of the plurality of users is obtained via a ledger interfacing program configured to indicate a plurality of user activity events involving each user in response to a ledger address associated with each user.
15. The non-transitory computer program product of claim 14, wherein the historical activity data for the plurality of users is obtained via the immutable distributed consensus ledger using a ledger address associated with each user of the plurality of users.
16. The non-transitory computer program product of claim 14, wherein the plurality of users associated with the token set includes a user associated with creation of the token set.
17. The non-transitory computer program product of claim 14, wherein the security classification for the token set is generated to indicate a predicted likelihood that the token set is not configured to perform malicious operations based at least on the length of time indicated by the historical activity data for each user being longer than a threshold time.
18. The non-transitory computer program product of claim 14, wherein the security classification for the token set is generated based at least on a respective security classification for at least one other token set of the plurality of token sets with which the plurality of users is associated.
19. The non-transitory computer program product of claim 14, wherein the historical activity data for the plurality of users includes an average length of time that a user of the plurality of users has been contiguously linked to a cryptographic token.
20. The non-transitory computer program product of claim 14, wherein the operations further comprise updating a graphical display of cryptographic tokens of the token set to reflect the security classification for the token set.
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July 20, 2022
December 10, 2024
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